Early identification of autism spectrum disorder (ASD) and subsequent engagement in evidence-based interventions is associated with substantial developmental gains and lower lifetime costs. Despite core symptoms emerging in the first year of life, the national average age of ASD diagnosis is not until 4 to 5 years, with diagnosis of children from lower income, minority, and rural families lagging further behind. Thus, there is a critical public health need to develop and test models of accurate, streamlined community-based ASD diagnosis. Much of the research to date has focused on independent examination of community-based models of early ASD diagnosis and measures of underlying biological processes as alternative approaches to identifying children with ASD. However, given the heterogeneous nature of the ASD phenotype as well as limitations in standard diagnostic tools, multi-method approaches that integrate clinical and biobehavioral measures are likely to have the most impact on advancing the accuracy of ASD diagnosis in the community setting. Our objective is to test an innovative method of ASD diagnosis that integrates clinical evaluation and assessment of biobehavioral markers in a large high-risk community-referral sample of children in the primary care setting. We propose three specific aims: 1) Evaluate the diagnostic accuracy of the Early Evaluation (EE) Hub model of ASD diagnosis in the community primary care setting, 2) Determine whether biobehavioral markers can reliably differentiate young children with and without ASD in a high-risk community referred sample, and 3) Determine whether a combination of clinical and biobehavioral measures can be used to accurately predict ASD diagnostic outcome in a high-risk sample of young children evaluated in the primary care setting. EE Hubs across the state of Indiana will refer a consecutive sample of 120 children, ages 16-30 months, for diagnostic confirmation by an expert ASD-specialist using a standardized protocol including the Autism Diagnostic Observation Schedule ? 2 as well as measures of developmental level and adaptive skills. A series of eye-tracking measures (pupil dilation, pupillary light reflex, blink rate, saccadic latency, and looking time) will provide indirect measures of neuromodulator activity (i.e., norepinephrine, acetylcholine, and dopamine, respectively) and non-social attentional disengagement efficiency and preferences for social compared to non-social stimuli. Our approach demonstrates a high level of scientific innovation because it integrates both clinical evaluation and assays of biobehavioral markers to develop and test a model of early ASD diagnosis in local primary care settings. The proposed research is significant because it has the potential to decrease wait times for initial ASD diagnosis and allow for earlier entry into evidence-based interventions, thereby improving child outcomes and reducing societal costs associated with the disorder. In the future, these clinical and biobehavioral profiles could be used to predict how an individual may respond to interventions, allowing for a more precise and cost effective method for intervention allocation.